In-air Knotting of Rope by a Dual-arm Multi-finger Robot

Kudoh, Shinzoh; Gomi, Tomoyuki; Katano, Ryota; Tomizawa, Tetsuo; Suehiro, Takashi

doi:10.1109/iros.2015.7354262

Cited by 20 publications

(14 citation statements)

References 9 publications

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“…However, the object's behavior is not modeled and, thus, the success of the approach depends only on a open-loop execution of these primitives by the robot. Kudoh et al (2015) extended these manipulation primitives to tie a knot in the air with two dexterous robotic hands (i.e. the fingers are actively controlled).…”

Section: Discretementioning

confidence: 99%

Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

Sánchez

Ramón

Bouzgarrou

et al. 2018

The International Journal of Robotics Research

391

228

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We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medical (e.g. surgery assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state of the art and propose future directions within the proposed classification.

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Section: Discretementioning

confidence: 99%

Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

Sánchez

Ramón

Bouzgarrou

et al. 2018

The International Journal of Robotics Research

391

228

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“…Early imitation learning works focus on extracting reference motion or primitives for reproducing deformable tube plugging (109) or in-air knotting (110). The demonstrations are collected through teleoperation.…”

Section: Imitation Learningmentioning

confidence: 99%

Modeling, learning, perception, and control methods for deformable object manipulation

2021

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Perceiving and handling deformable objects is an integral part of everyday life for humans. Automating tasks such as food handling, garment sorting, or assistive dressing requires open problems of modeling, perceiving, planning, and control to be solved. Recent advances in data-driven approaches, together with classical control and planning, can provide viable solutions to these open challenges. In addition, with the development of better simulation environments, we can generate and study scenarios that allow for benchmarking of various approaches and gain better understanding of what theoretical developments need to be made and how practical systems can be implemented and evaluated to provide flexible, scalable, and robust solutions. To this end, we survey more than 100 relevant studies in this area and use it as the basis to discuss open problems. We adopt a learning perspective to unify the discussion over analytical and data-driven approaches, addressing how to use and integrate model priors and task data in perceiving and manipulating a variety of deformable objects.

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“…In studies that utilize sensor information other than vision, Gomi et al [20] performed in-air knotting of rope by utilizing the RGDB camera. Seo et al [21] performed tasks by using the same robot hand in [20], RGBD camera, and torque sensors. During the knotting of rope, the rope state was in constant flux, and occlusions by the robot hand often occurred.…”

Section: A Utilization Of Input Modality In Knotting Of Ropementioning

confidence: 99%

“…Versatility is also important for the manipulation of flexible objects. Several existing studies have reported the robotic execution of knotting tasks using dedicated work environments [18][19] or hardware [20] [21]. However, even though they are realistic approaches for performing particular tasks, they are not versatile as it is difficult to use them for other similar tasks or environments.…”

Section: Introductionmentioning

confidence: 99%

In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning

Suzuki¹,

Kanamura²,

Suga³

et al. 2021

Preprint

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In this study, we report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. Owing to its flexibility, the state of the rope was in constant flux during the operation of the robot. This required the robot control system to dynamically correspond to the state of the object at all times. However, a manual description of appropriate robot motions corresponding to all object states is difficult to be prepared in advance. To resolve this issue, we constructed a model that instructed the robot to perform bowknots and overhand knots based on two deep neural networks trained using the data gathered from its sensorimotor, including visual and proximity sensors. The resultant model was verified to be capable of predicting the appropriate robot motions based on the sensory information available online. In addition, we designed certain task motions based on the Ian knot method using the dual-arm two-fingers robot. The designed knotting motions do not require a dedicated workbench or robot hand, thereby enhancing the versatility of the proposed method. Finally, experiments were performed to estimate the knotting performance of the real robot while executing overhand knots and bowknots on rope and its success rate. The experimental results established the effectiveness and high performance of the proposed method.

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In-air Knotting of Rope by a Dual-arm Multi-finger Robot

Cited by 20 publications

References 9 publications

Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

Modeling, learning, perception, and control methods for deformable object manipulation

In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning

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